Interdisciplinary approaches to image processing for medical robotics.

INTRODUCTION

The advancement of medical robotic systems highlights the critical need for precise and high-quality visual data, particularly in low-quality imaging scenarios. This study explores the interdisciplinary physics underlying image fusion and analysis, addressing challenges such as integrating complementary features, handling dynamic range variations, and suppressing noise in real-world medical contexts.

METHODS

We introduce the Multi-Scale Feature Adaptive Fusion Network (MFAFN) and the Dynamic Feature Refinement Strategy (DFRS), which leverage principles from computational and experimental physics to enhance imaging techniques. MFAFN applies multi-scale feature extraction, attention-based alignment, and adaptive fusion to improve spatial and spectral integration while preserving crucial details. Complementing this, DFRS employs saliency-based weighting, context-aware mechanisms, and dynamic normalization to refine feature importance and mitigate inconsistencies.

RESULTS

This interdisciplinary approach bridges computational physics, non-linear systems, and technological development, delivering significant improvements in fusion quality metrics such as spatial consistency, edge retention, and noise suppression.

DISCUSSION

Our findings contribute to advancing medical robotics by integrating novel physical principles into imaging methodologies, supporting sustainable innovations in healthcare technology.